Creating estimated electron density maps from magnetic resonance (MR) images is a problem that occurs e.g. with integrated PET/MR systems, where an attenuation maps needs to be estimated, or with radiation therapy planning based on MR, where electron density maps are required for the treatment simulation. However, due to the physics of the image acquisition, MR intensities do not uniquely correspond to electron density, hence those maps cannot be derived from the MR image by a simple lookup operation, as is commonly done when estimating these maps from CT images.
For radiotherapy planning, attenuation coefficients are required for dose calculation. Today, these coefficients are derived from CT images' HU values. However, increasingly often MR-images are acquired for diagnostic purpose or organ delineation prior to treatment planning Dose calculation based on only MR images would be highly beneficial in those cases, as this would eliminate the need for additional CT images for dose calculation and thus simplify the workflow and reduce radiation dose. For the development of MR-Linac systems with online treatment planning, an MR based dose calculation will be essential.
There are approaches known from the literature addressing the problem of estimating pseudo CT HU values from MR images. It is an insight of the invention that those still suffer from shortcomings. Specific problems with the different approaches are explained below.
For instance, registration of a CT based density atlas to the MR image may help in regions, where the atlas values are confined and reliable, e.g. the brain. However, in highly variable anatomical regions like the pelvic region, registration may not be able to cover the anatomical variations between patients, e.g. bladder/bowel filling or movement, resection of structures (e.g. kidneys, liver parts) or pathologic changes.
Other approaches divide the MR image into a number of tissue classes, e.g. bone, tissue and air, and assign average CT values to each tissue classes to simulate a CT image. The resulting image represents the patient anatomy at the tissue boundaries, however, the bulk values per tissue class ignore fine differences and structure within the tissue. Especially in bone tissue, CT intensities vary highly between cortical bone, trabecular structure and marrow parts. Further, partial volume effects are ignored, which is especially problematic for thin structures, e.g. the cortical bone.
A third class of approaches models a joint intensity distribution between CT numbers and a set of MR images and features derived from the MR images. The underlying assumption is that each tissue class has specific values in each of the MR images and can be modeled by a multi-dimensional Gaussian intensity value distribution. A multivariate Gaussian mixture model for the joint distribution of intensities is trained from images of different patients and then applied to the MR images and image-derivates of a new patient. To account for tissue-mixtures and for partial volume effects, e.g. tissue or voxels containing both bone and fat, more Gaussians than expected anatomical tissue types are used for the model. However, this is a definite drawback of the method, since only tissue-mixture types represented by a separate Gaussian can be assigned reliably. Further, to train the joint intensity distribution model a good correspondence between registered CT positions and MR positions is necessary. This may be achievable in the head, where there is little anatomical variation between acquisitions, however, for highly variable regions, such as the pelvis, this strong correlation of intensity values for training may not be achievable.
A common approach is to prescribe an average HU value to tissues derived from the MR image. Another common approach is to interpolate between different MR imaging contrasts.
WO2013144799A1 describes a magnetic resonance system that generates an attenuation or density map. The system includes a MR scanner defining an examination volume and at least one processor. The at least one processor is programmed to control the MR scanner to apply imaging sequences to the examination volume. In response to the imaging sequences, MR data sets of the examination volume are received and analyzed to identify different tissue and/or material types found in pixels or voxels of the attenuation or density map. One or more tissue-specific and/or material-specific attenuation or density values are assigned to each pixel or voxel of the attenuation or density map based on the tissue and/or material type(s) identified as being in each pixel or voxel during the analysis of the MR data sets.
The journal article Kawaguchi et. al., “A proposal for PET/MRI attenuation correction with μ-values measured using a fixed-position radiation source and MRI segmentation.” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 734 (2014): 156-161. doi:10.1016/j.nima.2013.09.015 discloses a method of determing individual μ-values for the brain. The μ-values are determined by placing a radiation source in a fixed position within a PET/MRI scanner. A MRI image of a subject is segmented into tissues posseing homogeneous μ-values and the radiation attenuation by the subject is measured. The measured radiation attenuation is then used to assigne the μ-values to the homogeneous regions.